{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.8.12 64-bit ('pytorchL': conda)' requires ipykernel package.\n",
      "Run the following command to install 'ipykernel' into the Python environment. \n",
      "Command: 'conda install -n pytorchL ipykernel --update-deps --force-reinstall'"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import os\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import torch.nn as nn \n",
    "import argparse, os, random\n",
    "from argparse import Namespace\n",
    "from torch.utils.data import DataLoader#,Subset, dataloader,random_split\n",
    "import numpy as np\n",
    "\n",
    "from core.data_loader import T4SALoaderVT\n",
    "from core.model import SoftGateDecentHeter\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class save_lambda(object):\n",
    "    def __init__(self) -> None:\n",
    "        super().__init__()\n",
    "        self.lamb_x = []\n",
    "        self.lamb_y = []\n",
    "    \n",
    "    def __call__(self, module, inputs, outputs):\n",
    "        lamb_x, lamb_y, _, _ = outputs\n",
    "        self.lamb_x.append(lamb_x.detach().cpu().numpy())\n",
    "        self.lamb_y.append(lamb_y.detach().cpu().numpy())\n",
    "\n",
    "    def get_lambda(self):\n",
    "        return np.concatenate(self.lamb_x, axis=0), \\\n",
    "               np.concatenate(self.lamb_y, axis=0)\n",
    "\n",
    "    def save(self, path=None):\n",
    "        np.savez_compressed(\n",
    "            path,\n",
    "            lamb_x = np.concatenate(self.lamb_x, axis=0),\n",
    "            lamb_y = np.concatenate(self.lamb_y, axis=0)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed):\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed(seed)\n",
    "        torch.cuda.manual_seed_all(seed)  \n",
    "    np.random.seed(seed)  \n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.deterministic = True \n",
    "\n",
    "def top_pred(x, y, concat):\n",
    "    new_array = []\n",
    "    for i,j,o in zip(x,y,concat):\n",
    "        if np.max(i)>np.max(j) and np.max(i)>np.max(o):\n",
    "            new_array.append(list(i))\n",
    "        elif np.max(j)>np.max(i) and np.max(j)>np.max(o):\n",
    "            new_array.append(list(j))\n",
    "        else:\n",
    "            new_array.append(list(o))\n",
    "\n",
    "    return np.array(new_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "arg_dict = {\n",
    "    \"mark\" : \"95.52745098039216_1915\",\n",
    "    \"layer\" : 4,\n",
    "    \"hidden_size\" : 1024,\n",
    "    \"dim\" : 1024,\n",
    "\n",
    "    \"dropout_r\" : 0.1,\n",
    "    \"multi_head\" : 8,\n",
    "    \"ff_size\" : 2048,\n",
    "    \"word_embed_size\" : 300,\n",
    "\n",
    "    \"image_hw\" : 256,\n",
    "    \"patch_hw\" : 32,\n",
    "    \"lang_size\" : 31,\n",
    "    \"num_classes\" : 3,\n",
    "    \"img_len\" : 65,\n",
    "    \"text_len\" : 31,\n",
    "    \n",
    "    \"device\" : 'cuda:1',\n",
    "    \"output\" : 'ckpt/',\n",
    "    \"name\" : 'exp_cofeature/',\n",
    "    \"batch_size\" : 128,\n",
    "    \"seed\" : 1915,\n",
    "    \"ans_size\" : 3,\n",
    "}\n",
    "\n",
    "\n",
    "args = Namespace(**arg_dict)\n",
    "set_seed(args.seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating train language files\n"
     ]
    }
   ],
   "source": [
    "train_dataset = T4SALoaderVT(0, None, args) \n",
    "test_dataset =T4SALoaderVT(2, train_dataset.token_to_ix, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loaded from ../ckpt/exp_cofeature/best95.52745098039216_1915.pkl\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Multi_Fusion_Model_Decent_CoFeature(\n",
       "  (to_patch_embedding): Sequential(\n",
       "    (0): Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=32, p2=32)\n",
       "    (1): Linear(in_features=3072, out_features=1024, bias=True)\n",
       "  )\n",
       "  (embedding): Embedding(21585, 300)\n",
       "  (adapter_text): Linear(in_features=300, out_features=1024, bias=True)\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (to_latent): Identity()\n",
       "  (mlp_concat): Sequential(\n",
       "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       "  (mlp_x): Sequential(\n",
       "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       "  (mlp_y): Sequential(\n",
       "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       "  (enc_list): ModuleList(\n",
       "    (0): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (2): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (3): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_iter = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4)\n",
    "    \n",
    "device = torch.device(args.device)\n",
    "\n",
    "net = SoftGateDecentHeter(args, train_dataset.vocab_size, train_dataset.pretrained_emb)\n",
    "if args.mark is None:\n",
    "    mpath = os.path.join(\"..\", args.output, args.name, 'best'+str(args.seed)+'.pkl')\n",
    "else:\n",
    "    mpath = os.path.join(\"..\", args.output, args.name, 'best'+ args.mark +'.pkl')\n",
    "print(f\"model loaded from {mpath}\")\n",
    "net.load_state_dict(torch.load(mpath)['state_dict'])\n",
    "net.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = {i: save_lambda() for i in range(args.layer)}\n",
    "for i in range(args.layer):\n",
    "    net.enc_list[i].softgate.register_forward_hook(saver[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "399it [04:22,  1.52it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Multi_Fusion_Model_Decent_CoFeature(\n",
       "  (to_patch_embedding): Sequential(\n",
       "    (0): Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=32, p2=32)\n",
       "    (1): Linear(in_features=3072, out_features=1024, bias=True)\n",
       "  )\n",
       "  (embedding): Embedding(21585, 300)\n",
       "  (adapter_text): Linear(in_features=300, out_features=1024, bias=True)\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (to_latent): Identity()\n",
       "  (mlp_concat): Sequential(\n",
       "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       "  (mlp_x): Sequential(\n",
       "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       "  (mlp_y): Sequential(\n",
       "    (0): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "    (1): Linear(in_features=1024, out_features=3, bias=True)\n",
       "  )\n",
       "  (enc_list): ModuleList(\n",
       "    (0): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (2): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (3): BlockHeter(\n",
       "      (mhatt_x): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_x): Dropout(p=0.1, inplace=False)\n",
       "      (norm_x): LayerNorm()\n",
       "      (mhatt_y): MHAtt(\n",
       "        (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (dropout_y): Dropout(p=0.1, inplace=False)\n",
       "      (norm_y): LayerNorm()\n",
       "      (fb1): FeedLayerVit(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (fb2): FeedLayer(\n",
       "        (ffn): FFN(\n",
       "          (mlp): MLP(\n",
       "            (fc): FC(\n",
       "              (linear): Linear(in_features=1024, out_features=2048, bias=True)\n",
       "              (relu): ReLU(inplace=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (linear): Linear(in_features=2048, out_features=1024, bias=True)\n",
       "          )\n",
       "        )\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (norm1): LayerNorm()\n",
       "      )\n",
       "      (softgate): SoftGate(\n",
       "        (conv1d): Conv1d(1024, 10, kernel_size=(1,), stride=(1,))\n",
       "        (max_pool): MaxPool2d(kernel_size=(5, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)\n",
       "        (layer): Sequential(\n",
       "          (0): Linear(in_features=192, out_features=64, bias=True)\n",
       "          (1): ReLU()\n",
       "          (2): Dropout(p=0.1, inplace=False)\n",
       "          (3): Linear(in_features=64, out_features=2, bias=True)\n",
       "        )\n",
       "        (sa): MHAtt(\n",
       "          (linear_v): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_k): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_q): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (linear_merge): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = []\n",
    "net.train(False)\n",
    "preds = []\n",
    "accs= [[], [], [], []]\n",
    "for step, (\n",
    "        x,\n",
    "        y,\n",
    "        y_mask,\n",
    "        xy_mask,\n",
    "        ans\n",
    ") in tqdm(enumerate(test_data_iter)):\n",
    "    x = x.to(device)\n",
    "    # x = torch.zeros_like(x).to(device)\n",
    "    y = y.to(device)\n",
    "    y_mask = y_mask.to(device)\n",
    "    xy_mask = xy_mask.to(device)\n",
    "\n",
    "    x_pred, y_pred, concat_pred = net(x, y, y_mask, xy_mask)\n",
    "\n",
    "    x_pred = x_pred.cpu().data.numpy()\n",
    "    y_pred = y_pred.cpu().data.numpy()\n",
    "    concat_pred = concat_pred.cpu().data.numpy()\n",
    "\n",
    "    fin_pred = top_pred(x_pred, y_pred, concat_pred)\n",
    "    \n",
    "    ans = ans.cpu().data.numpy()\n",
    "    accuracy += list(np.argmax(fin_pred, axis=1) == ans)\n",
    "\n",
    "    accs[0] += list(np.argmax(x_pred, axis=1) == ans)\n",
    "    accs[1] += list(np.argmax(y_pred, axis=1) == ans)\n",
    "    accs[2] += list(np.argmax(concat_pred, axis=1) == ans)\n",
    "    accs[3] += list(np.argmax(fin_pred, axis=1) == ans)\n",
    "        \n",
    "    # Save preds\n",
    "    # for p in pred:\n",
    "    #     preds.append = p\n",
    "\n",
    "net.train(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[95.38627451 95.41568627 95.38823529 95.41568627]\n"
     ]
    }
   ],
   "source": [
    "acc = 100*np.mean(np.array(accs), axis=-1)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import torch \n",
    "import matplotlib.pyplot as plt \n",
    "import torch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lambda(data):\n",
    "    lambd_x = []\n",
    "    lambd_y = []\n",
    "    for i in data:\n",
    "        lambd_x.extend(i[:,0])\n",
    "        lambd_y.extend(i[:,1])\n",
    "\n",
    "    lambd_x = torch.sigmoid(torch.tensor(lambd_x))\n",
    "    lambd_y = torch.sigmoid(torch.tensor(lambd_y))\n",
    "\n",
    "    lambd_x = lambd_x.numpy().tolist()\n",
    "    lambd_y = lambd_y.numpy().tolist() \n",
    "    return lambd_x,lambd_y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lambda(path_root,name):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    fig,subs=plt.subplots(4,2,sharex=False)\n",
    "    \n",
    "    for i in range(4):\n",
    "        lambd_data = np.load(path_root+name+\"_{}.npy\".format(i),allow_pickle=True)\n",
    "        lambdx,lambdy = get_lambda(lambd_data)\n",
    "\n",
    "        subs[i][0].hist(lambdx, bins=10, density=False)\n",
    "        subs[i][1].hist(lambdy, bins=10, density=False)\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/mosei_arch/\"\n",
    "name = \"mosei_decent_normal_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/mosei_arch/\"\n",
    "name = \"mosei_decent_pad_x_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/mosei_arch/\"\n",
    "name = \"mosei_decent_pad_y_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/mosei_decent/\"\n",
    "name = \"mosei_decent_pad_x_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/mosei_decent/\"\n",
    "name = \"mosei_decent_pad_y_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/mosei_decent/\"\n",
    "name = \"mosei_decent_normal_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/vsnli_decent/\"\n",
    "name = \"normal_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/vsnli_decent/\"\n",
    "name = \"pad_x_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/vsnli_decent/\"\n",
    "name = \"pad_y_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/vsnli_arch/\"\n",
    "name = \"normal_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/vsnli_arch/\"\n",
    "name = \"pad_x_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/vsnli_arch/\"\n",
    "name = \"pad_y_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/t4sa_arch/\"\n",
    "name = \"normal_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/t4sa_arch/\"\n",
    "name = \"pad_x_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/t4sa_arch/\"\n",
    "name = \"pad_y_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAD6CAYAAACyNXAiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAebElEQVR4nO3de7BV5Znn8e8voI6dAS/cBvFydEIqBzRhAMUkVpeWZUSdSBQywVATp4suEpPMZIZpK9h/pPOPDqa0W50xSTPGUsnES9mTwYkQtUNPpxpFPURRLiGingSE4iKODZlJAswzf6z34Gaf28s5+7L2Pr9P1a6z9rvfd61nbZ7Ns9Zea6+liMDMzGwwH2p2AGZm1hpcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyyZBUMSd2SXpf0qqSu1HampOckvZH+nlHR/zZJ2yVtk3R1RfusNJ/tku6TpNR+iqTHU/uLkjpqvJ5mZjZMyvkdhqRuYHZE7K9o+w5wICKWS1oGnBER35Q0DXgUuAQ4C/hb4KMRcVTSS8A3gPXAauC+iFgj6avAxyPiK5IWAjdExBcGimn8+PHR0dExhFU2+8Drr7/Ohz70ISQhic7OTo4cOcLGjRsPA78GuoF/FRHvQbExBCwGjgL/LiKeSe2zgIeAUyly+xsREZJOAR4BZgHvAl+IiO6BYnJuWz1t2LBhf0RMGNLgiBj0QfGhGV/Vtg2YnKYnA9vS9G3AbRX9ngE+mfr8sqL9JuCvK/uk6dHAflIx6+8xa9asMBuu8847L/bt23dc26233hrAzijycRlwZ5qeBmwETgHOB94ERqXXXkp5LmANcE1q/yrw/TS9EHg8Bvm8ObetnoCuyPh/v69H7jGMAJ6VtEHSktQ2KSJ2p6KzG5iY2qcAOyrG7kxtU9J0dftxYyLiCPA+MC4zNrOaWrVqFRR7AwAPA59L0/OAxyLi9xHxNrAduETSZGBsRLyQPpCPVI15OE0/CVzZ81WsWasZndnv0xGxS9JE4DlJvxygb18fhhigfaAxx8+4KFZLAM4999x+A+hY9vQA4fWve/l1QxpnrUsSn/nMZ5DEl7/8ZZYsWcKePXsADkOxMZTyHooNm/UVw3s2eg6TuTEkqWdjaH9F/7rmtvPaaiWrYETErvR3r6QfUxyf2CNpcvpATQb2pu47gXMqhp8N7ErtZ/fRXjlmp6TRwGnAgT7iWAGsAJg9e7YvgmXDtm7dOs466yz27t3LVVddxcc+9rGButdtY8i5ba1g0K+kJH1Y0pieaeAzwCbgKeDm1O1mYFWafgpYmM58Oh+YCryUvrY6KOnStEv+paoxPfNaAKxNu/ZmdXXWWWcBMHHiRG644QZeeuklJk2aBHASQA03hhhoY8isFeQcw5gE/IOkjRQH9p6OiJ8Cy4GrJL0BXJWeExGbgSeALcBPga9FxNE0r1uAByi++32T4uAgwA+AcZK2A0spDjSa1dVvf/tbDh48eGz62Wef5cILL+T666+HD46heWPILBn0K6mIeAv4RB/t7wJX9jPmduD2Ptq7gAv7aP8d8PmMeM1qZs+ePdxwww0AHDlyhC9+8YvMnTuXiy++mLvuumts2hj6DSk3I2KzpJ6NoSP03hh6iOK02jUcvzG0Mm0MHaA4U8qsJeUe9DZrOxdccAEbN27s1T5u3DiAX0XE7OrXvDFkI5kvDWJmZllcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyyuGCYmVkWFwwzM8vigmFmZllcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyyuGCYmVkWFwwzM8vigmFmZllcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyyuGCYmVkWFwwzM8vigmFmZllcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyyuGCYmVkWFwwzM8vigmFmZllcMMzMLIsLhpmZZSlNwZA0V9I2SdslLWt2PGa14ty2dlGKgiFpFHA/cA0wDbhJ0rTmRmU2fM5tayelKBjAJcD2iHgrIv4APAbMa3JMZrXg3La2MbrZASRTgB0Vz3cCcxodRMeyp4c0rnv5dTWOxNpIKXLbrBbKUjDUR1v06iQtAZakp4ckbetnfuOB/TWKbVC6c8hDGxrnMLRKnFC7WM+rwTygBLk9jPxslFbKrxNR1vUacm6XpWDsBM6peH42sKu6U0SsAFYMNjNJXRExu3bh1YfjrL0Sxjoic/tEtOM6QXuuV1mOYbwMTJV0vqSTgYXAU02OyawWnNvWNkqxhxERRyR9HXgGGAU8GBGbmxyW2bA5t62dlKJgAETEamB1jWY36K59STjO2itdrCM0t09EO64TtOF6KaLX8TczM7NeynIMw8zMSq6lC8Zgl1xQ4b70+muSZpY0zkUpvtckPS/pE2WMs6LfxZKOSlrQyPgqlj9onJIul/SqpM2S/r7RMQ5Hq+T1icpYr49JekHS7yX9WTNiHIpW+XzXRES05IPiAOKbwAXAycBGYFpVn2uBNRTnwl8KvFjSOD8FnJGmrylrnBX91lJ8J7+gjHECpwNbgHPT84nNyNE6rl/T87pO6zURuBi4HfizZsdcw/Vq+ue7Vo+WPYYxfvz46OjoaHYY1qY2bNiwPyImNHq5kj45bty4553bVi/Dye3SnCV1ojo6Oujq6mp2GNamJP26SYue4ty2ehpObrdswRiIrwllLayvS4kcM5Tcdl5brbT0QW+zNrSz2QGY9ccFw6xcXm52AGb9ccEwK5GIONLsGMz644JhZmZZXDDMzCyLC4aZmWVxwTAzsywuGGZmlsUFw8zMsrhgmJlZFhcMMzPL4oJhZmZZXDDMzCzLoAVD0jmS/k7S1nT3sm+k9jMlPSfpjfT3jIoxt6W7T22TdHVF+yxJr6fX7pOk1H6KpMdT+4uSOuqwrmbH2bFjB1dccQWdnZ1Mnz6de++9F4ADBw4ATHVumx0vZw/jCPAfI6KT4u5eX5M0DVgG/CwipgI/S89Jry0EpgNzge9KGpXm9T1gCTA1Peam9sXAexHxEeCvgDtrsG5mAxo9ejR33303W7duZf369dx///1s2bKF5cuXAxx0bpsdb9CCERG7I+IXafogsBWYAswDHk7dHgY+l6bnAY9FxO8j4m1gO3CJpMnA2Ih4IYrb/D1SNaZnXk8CV/ZsoZnVy+TJk5k5s7gd9pgxY+js7OSdd95h1apVAO+mbs5ts+SEjmGk3el/AbwITIqI3VAUFYr78UJRTHZUDNuZ2qZw/LX+e9qPG5Ou1vk+MO5EYjMbju7ubl555RXmzJnDnj17AA6Dc9usUnbBkPRPgb8B/n1E/ONAXftoiwHaBxpTHcMSSV2Suvbt2zdYyGZZDh06xPz587nnnnsYO3bsQF2d2zaiZRUMSSdRFIv/FhH/PTXvSbvipL97U/tO4JyK4WcDu1L72X20HzdG0mjgNOBAdRwRsSIiZkfE7AkThnQPc7PjHD58mPnz57No0SJuvPFGACZNmgRwEji3zSrlnCUl4AfA1oj4y4qXngJuTtM3A6sq2hems0POpzgA+FLatT8o6dI0zy9VjemZ1wJgbfou2KxuIoLFixfT2dnJ0qVLj7Vff/318MHXRs5ts2R0Rp9PA/8aeF3Sq6ntz4HlwBOSFgO/AT4PEBGbJT0BbKE4w+prEXE0jbsFeAg4FViTHlAUpJWStlNsfS0c3mqZDW7dunWsXLmSiy66iBkzZgBwxx13sGzZMu66666xkt7AuW12zKAFIyL+gb6/hwW4sp8xtwO399HeBVzYR/vvSB9Ks0a57LLLGGBj/1cRMbu60bltI5l/6W1mZllcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyyuGCYmVkWFwwzM8vigmFmZllcMMzMLIsLhpmZZXHBMDOzLC4YZmaWxQXDzMyy5NwPY8ToWPb0kMZ1L7+uxpGYmZWP9zDMzCyLC4aZmWVxwTAzsyw+hlEDQzn24eMe1ig+Nme1UpqCIWkucC8wCnggIpY3OaS68od45Bhpud3Ohvq5Haqyfd5LUTAkjQLuB64CdgIvS3oqIrY0N7LyaXTCDlXZEr1ZWjm3WyXX2lnZNixLUTCAS4DtEfEWgKTHgHlA6T9U1reRviVWwbltbaMsBWMKsKPi+U5gTnUnSUuAJenpIUnb+pnfeGB/TSPM16xlj8R1PrZs3Vnz+Z5Xo/m0U25XK1MsUK54mhpLH5+HyniGnNtlKRjqoy16NUSsAFYMOjOpKyJm1yKwE9WsZY/EdW72sjO1TW5XK1MsUK54yhQL1C6espxWuxM4p+L52cCuJsViVkvObWsbZSkYLwNTJZ0v6WRgIfBUk2MyqwXntrWNUnwlFRFHJH0deIbi1MMHI2LzMGY56K59HTVr2SNxnZu97EG1WW5XK1MsUK54yhQL1CgeRfT6OtXMzKyXsnwlZWZmJeeCYWZmWVq6YEiaK2mbpO2SlvXxuiTdl15/TdLMBi13UVrea5Kel/SJWiw3Z9kV/S6WdFTSgkYuW9Llkl6VtFnS3zdiuZJOk/Q/JW1My/2TWiy3kYaTy7k5UeN4+s1xSd2SXk950NWAWC6X9H5a3quSvpU7tk7x3FoRy6b0OTwzvVbr9+ZBSXslbern9drmTUQM+gC6gdeBV4Gu1HYm8BzwRvp7RkX/24DtwDbg6or2WWk+24H7+OAYyinA46n9RaAjI6ZRwJvABcDJwEZgWlWfa4E1FOfCXwq8mLO+NVjup3reD+CaWiw3d9kV/dYCq4EFjVo2cDrFL5jPTc8nNmi5fw7cmaYnAAeAk2ux3o14DCeXc3OikTlO8f/F+Aa+N5cDPxnK2HrEU9X/s8Daerw3aX5/DMwENvXzek3zJuugt6RuYHZE7K9o+w5wICKWp+p0RkR8U9I04FGKSyKcBfwt8NGIOCrpJeAbwHqK/8zui4g1kr4KfDwiviJpIXBDRHxhoJjGjx8fHR0dg8ZuNhQbNmzYHxETGrEsSZ8Evh0RV4Nz2+prw4YNB4B/SUXOSboNICL+00Bjh3Na7TyKyg7wMPC/gG+m9sci4vfA25K2A5ekojM2Il5IAT4CfI6i+s0Dvp3m9STwXyQpBqhmHR0ddHUNe4/OrE+Sft3AxR13+RDnttWTpD1kXrKmWm7BCOBZSQH8dRSXMZgUEbsBImK3pImp7xSKPYjKQKYAh9N0dXvPmB1pXkckvQ+MozzXhbGSa/F7kvR1+ZA+tfh6WnlkXbKmWm7B+HRE7EpF4TlJvxxCIAMFmBW8Ki7Qdu655w4csVnrqL58iFk99Wy8n/Ala7LOkoqIXenvXuDHFMcn9kiaDJD+7k3d+wtkZ5ruK8BjYySNBk6jOHBZHceKiJgdEbMnTGjI18tmjVB9+RCzejrMEC9ZM2jBkPRhSWN6poHPAJvSzG9O3W4GVqXpp4CFkk6RdD4wFXgpfX11UNKlkgR8qWpMz7wWUJxV4J+g24gQEUeAnsuHbG1yODYC9JFzT0TGJWtyvpKaBPy4+D+e0cCPIuKnkl4GnpC0GPgN8PkUyGZJT1CcXnkE+FpEHE3zugV4CDiV4mD3mtT+A2BlOkB+gKLamY0YEbGa4sxBZs+e7Y0lq7vKnMs1aMGI4k5hvX54FhHvAlf2M+Z24PY+2ruAC/to/x2p4JiZWTm19C+9zcyscVwwzMwsiwuGmZllccEwM7MsLhhmZpbFBcPMzLK4YJiZWRYXDDMzy+KCYSPWjh07uOKKK+js7GT69Once++9AHz7298G+HjFXdOu7Rkj6bZ0h7Jtkq6uaJ+V7qS2Pd3hTKn9FEmPp/YXJXU0dCXNamg498Mwa2mjR4/m7rvvZubMmRw8eJBZs2Zx1VVX9by8JyJmVPZPNwdbCEwn3RxM0kfTpW++R3El5Z6bg82luPTNYuC9iPhIujnYncCANwczKyvvYdiINXnyZGbOLG5xPGbMGDo7O3nnnXcGGnLs5mAR8TbFLYUvSVdrHhsRL6SLZvbcHKxnzMNp+kngyp69D7NW44JhBnR3d/PKK68wZ86xm45NlPSapAclnZHa+rpL2ZT0yLo5GNBzc7DjSFoiqUtS1759+2q1WmY15YJhI96hQ4eYP38+99xzD2PHjuWWW24BeB2YAewG7k5d63ZzMN/rxVqBC4aNaIcPH2b+/PksWrSIG2+8EYBJkyYBEBH/D/ivFDcMgzreHMysFbhg2IgVESxevJjOzk6WLl16rH337t2V3W6guGEY+OZgNsL5LCkbsdatW8fKlSu56KKLmDFjBgB33HEHjz76KMA0Sa8B3cCXwTcHM3PBsBHrsssuo6+N/WuvvZYf/vCHWyJidvVrvjmYjWT+SsrMzLK4YJiZWRYXDDMzy+KCYWZmWVwwzMwsiwuGmZllccEwM7MsLhhmZpbFBcPMzLK4YJiZWRYXDDMzy+KCYWZmWVwwzMwsiwuGmZllccEwM7MsLhhmZpbFBcPMzLK4YJiZWZbSFAxJcyVtk7Rd0rJmx2NWK85taxelKBiSRgH3A9cA04CbJE1rblRmw+fctnYyutkBJJcA2yPiLQBJjwHzgC1Njcps+JzbRseypxu6vO7l19VlvmUpGFOAHRXPdwJzmhSLWS01Pbcb/Z+Vta+yFAz10Ra9OklLgCXp6SFJ26q6jAf21zi2oXIsfStNLLpzwFjOq9Vi+mgbSm73KMv7V5Y4wLH0Uq/cLkvB2AmcU/H8bGBXdaeIWAGs6G8mkroiYnbtwztxjqVvIzCWmuR2j7K8f2WJAxxLI+MoxUFv4GVgqqTzJZ0MLASeanJMZrXg3La2UYo9jIg4IunrwDPAKODBiNjc5LDMhs25be2kFAUDICJWA6uHOZtBd+kbyLH0bcTFUqPc7lGW968scYBj6Utd4lBEr+NvZmZmvZTlGIaZmZVcWxQMSedI+jtJWyVtlvSNEsQ0StIrkn7S5DhOl/SkpF+m9+eTTYzlP6R/n02SHpX0Txq47Acl7ZW0qaLtTEnPSXoj/T2jUfH0Ed+Alw9R4b70+muSZuaOrUMsi1IMr0l6XtInKl7rlvS6pFclddU5jsslvZ+W9aqkb+WOrUMst1bEsUnSUUlnptdq+Z70yuOq1+ubJxHR8g9gMjAzTY8BfgVMa3JMS4EfAT9pchwPA3+apk8GTm9SHFOAt4FT0/MngH/TwOX/MTAT2FTR9h1gWZpeBtzZpPdmFPAmcEH6N9pYnb/AtcAait91XAq8mDu2DrF8CjgjTV/TE0t63g2Mb9B7cnlfn69mvCdV/T8LrK31e9JfHjcyT9piDyMidkfEL9L0QWArxX9QTSHpbOA64IFmxZDiGEuRYD8AiIg/RMT/bmJIo4FTJY0G/og+fo9QLxHxc+BAVfM8ioJK+vu5RsVT5djlQyLiD0DP5UMqzQMeicJ64HRJkzPH1jSWiHg+It5LT9dT/Lak1oazXg1/T6rcBDw6jOX1q588rlTXPGnZg97jx4+Pjo6OZodhbWrDhg37I2JCI5YlaQEwNyL+FJzbVl8bNmzYT/ENyJyI+PqJjC3NabUnqqOjg66uYX0daNYvSb9u5OIqnzi3rZ4qcvuE9xZatmAMZKgXW6vXFR7NBlF9+ZB+DSW3ndfWhz4vUTOYtjiGYdbiqi8fYlZPYoiXqHHBMGuyiDgC9Fw+ZGuTw7H2Nx14IoZwiRoXDLMSiIjVEfHRiPjnzY7F2t6miLh9KANdMMzMLIsLhpmZZXHBMDOzLC4YZmaWpS1/h2Ejj3+fYFZ/3sMwM7MsLhhmZpbFBcPMzLK4YJiZWRYXDDMzy+KCYWZmWVwwbMTasWMHV1xxBZ2dnUyfPp17770XgAMHDkBx9dhe9/qWdFu6J/I2SVdXtM9K923enu6prNR+iqTHU/uLkjoau5ZmteOCYSPW6NGjufvuu9m6dSvr16/n/vvvZ8uWLSxfvhzgYERMBX5Gcb9vJE2juCz0dGAu8F1Jo9LsvgcsAaamx9zUvhh4LyI+AvwVcGeDVs+s5gYtGJIelLRX0qaKtjPTlpe3wKxlTZ48mZkzZwIwZswYOjs7eeedd1i1ahXAu6lb5b2+5wGPRcTvI+JtYDtwSbpn8tiIeCGKex4/UjWm577hTwJX9uS+WavJ2cN4iA+2lnosA37mLTBrF93d3bzyyivMmTOHPXv2ABwGiIjdwMTUbQqwo2LYztQ2JU1Xtx83Jt334n1gXJ1Ww6yuBi0YEfFz4EBVc+VWk7fArKUdOnSI+fPnc8899zB27NiBuvaVlzFA+0Bjjp+xtERSl6Suffv2DRqzWTMM9RjGpLTl5S0wa2mHDx9m/vz5LFq0iBtvvBGASZMmAZwEkDZ29qbu1ffe7rkv8s40Xd1+3BhJo4HT6L0BRkSsiIjZETF7woQJtVk5sxqr9UHvum2BgbfCrLYigsWLF9PZ2cnSpUuPtV9//fXwwUbLzcCqNP0UsDAddzuf4qvVl9JG00FJl6a94y9Vjbk5TS8A1qa9bLOWM9SCsSdteTVsCwy8FWa1tW7dOlauXMnatWuZMWMGM2bMYPXq1SxbtgxgrKQ3gKuA5QDpHshPAFuAnwJfi4ijaXa3AA9QfA37JrAmtf8AGCdpO7CUdLzPrBUN9fLmPVtNy+m9BfYjSX8JnMUHW2BHJR2UdCnwIsUW2H+umtcLeAvMGuiyyy5jgFT7VUTMrm5M90LudT/kiOgCLuyj/XfA54cZqlkpDFowJD0KXA6Ml7QT+AuKQvGEpMXAb0gfiIjYLKlnC+wIvbfAHgJOpdj6qtwCW5m2wA5QnGVlZmYlM2jBiIib+nnpyn76ewvMzKwN+ZfeZmaWxQXDzMyyuGCYmVkWFwwzM8vigmFmZllcMMzMLMtQf7jXljqWPT2kcd3Lr6txJGa147y2WvEehpmZZXHBMDOzLC4YZmaWxQXDzMyyuGCYmVkWnyVVA0M5C8VnoJhZq3HBsFIZ6imgZlZ/LhhN4nPjreyco1atNAVD0lzgXmAU8EBELG9ySKXkD3HrcW5buyhFwZA0Crif4v7JO4GXJT0VEVuaG1n78Fc9zeHctnZSioIBXAJsj4i3ACQ9BsyjuNWrWSsbcbntveD2VZaCMQXYUfF8JzCnSbGY1ZJzO5MLTfmVpWCoj7bo1UlaAixJTw9J2tbP/MYD+2sU23A5lt5KEYfuBPqP5bxaLaaPtnbJ7aGoefzp37GRWv3fYMi5XZaCsRM4p+L52cCu6k4RsQJYMdjMJHVFxOzahTd0jqW8cUBDYmnb3B6KVo8f2mMdhqosv/R+GZgq6XxJJwMLgaeaHJNZLTi3rW2UYg8jIo5I+jrwDMWphw9GxOYmh2U2bM5tayelKBgAEbEaWF2j2Q26a99AjqW3ssQBDYiljXN7KFo9fmiPdRgSRfQ6/mZmZtZLWY5hmJlZybVUwZA0V9I2SdslLevjdUm6L73+mqSZuWPrEMuiFMNrkp6X9ImK17olvS7pVUldDYjlcknvp+W9KulbuWPrEMutFXFsknRU0pnptZq9L5IelLRX0qZ+Xm9Yrpyo4eR5WQzn81EGuTkg6eKUwwsaGV/TRERLPCgOGL4JXACcDGwEplX1uRZYQ3Hu+6XAi7lj6xDLp4Az0vQ1PbGk593A+Aa+L5cDPxnK2FrHUtX/s8DaOr0vfwzMBDb183pDcqWReV6Wx3A/H81+5OZA6reW4vjUgmbH3YhHK+1hHLvEQkT8Aei5xEKlecAjUVgPnC5pcubYmsYSEc9HxHvp6XqK8+/rYTjr1vD3pcpNwKPDWF6/IuLnwIEBujQqV07UcPK8LMr0+RiK3Bz4t8DfAHsbGVwztVLB6OsSC1My++SMrXUslRZTbBH2COBZSRvSL3yHIzeWT0raKGmNpOknOLbWsSDpj4C5FB+4HrV8XwbTqFypVVwn2qeZhvv5aLZB45c0BbgB+H4D42q60pxWmyHnEgv99cm6PEONYyk6SldQfCAuq2j+dETskjQReE7SL9MWcb1i+QVwXkQcknQt8D+AqZljax1Lj88C6yKici+glu/LYBqVKydqOHleFsP9fDRbTvz3AN+MiKNSX93bUyvtYeRcYqG/PlmXZ6hxLEj6OPAAMC8i3u1pj4hd6e9e4McUu8B1iyUi/jEiDqXp1cBJksbnrkctY6mwkKqvo2r8vgymUblSq7hOtE8zDevzUQI58c8GHpPUDSwAvivpcw2JrpmafRAl90GxN/QWcD4fHIiaXtXnOo4/GPhS7tg6xHIusB34VFX7h4ExFdPPA3PrHMs/44Pf3FwC/Ca9Rw1/X1K/0yiOL3y4Xu9Lmk8H/R/0bkiuNDLPy/IYzuejDI8TzQHgIUbIQe+W+Uoq+rnEgqSvpNe/T3G2wrUUifh/gD8ZaGydY/kWMI5iywPgSBQXLJsE/Di1jQZ+FBE/rXMsC4BbJB0B/i+wMIpMb8b7AsV3v89GxG8rhtf0fZH0KMXZYeMl7QT+AjipIo6G5MqJGk6el8UwPx9NdwJ5POL4l95mZpallY5hmJlZE7lgmJlZFhcMMzPL4oJhZmZZXDDMzCyLC4aZmWVxwTAzsywuGGZmluX/A6KdnfzhFCPNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/t4sa_decent/\"\n",
    "name = \"normal_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/t4sa_arch/\"\n",
    "name = \"pad_x_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_root = \"lambda/t4sa_arch/\"\n",
    "name = \"pad_y_layer\"\n",
    "plot_lambda(path_root,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5662170271120be95561579294da924907f1629cd81697093e3a86d2a805376b"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 64-bit ('decentralized': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
